Goal: Automatically identify tumour and immune cells in H&E tissue images.
Challenge: Find model fit for usage in clinical settings.
Approach: Evaluate & compare 3 different classification models.
Outperformed Other Models
High Sensitivity (95%): High detection of tumour cells.
End-to-End Learning - No need to extract or select features
Automatic Feature Extraction - Finds more subtle image patterns hierarchically
Scalability - Can handle larger datasets with more cell types
Large data requirements
CNN “Black box” blurs interpretability
More computationally expensive than RF and SVM
Prone to over-fitting
Clinical Implications: CNN had the best sensitivity to tumour cells.
Professional Interpretation: Positive test results must be carefully examined by pathologists.
Future Direction: Look at classifying other cell types, examining other tissue and using different imaging systems
ChatGPT (OpenAI, 2023) was used for assistance in preparing this presentation template, debugging and searching for additional packages for models.